In Silico Investigation of Spice Molecules as Potent Inhibitor of SARS-CoV-2
Bibliographic record
Abstract
The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a novel infectious disease that is in rapid growth. Several trials are going on worldwide to find a solution for this pandemic. The viral replication can be blocked by inhibiting the SARS-CoV-2 spike protein (SARS-CoV-2 Spro), and the SARS-CoV-2 main protease (SARS-CoV-2 Mpro). The binding of potential small molecules to these proteins can possibly inhibit the replication and transcription of the virus. The spice molecules that are used in our food have the properties of antiviral, antifungal, and antimicrobial nature. As spice molecules are consumed in the diet, hence its antiviral properties against SARS-CoV-2 will benefit in a significant manner. Therefore, in this work, the blind molecular docking of 30 selected spice molecules (through ADME property screening) was performed for the identification of potential inhibitors for the Spro and Mpro of SARS-CoV-2. We found that all the molecules bind actively with the SARS-CoV-2 Spro and Mpro. However, the molecule, Piperine, is found to have the highest binding affinity among the 30 screened molecules. We anticipate immediate wet-lab experiments and clinical trials in support of this computational study might be helpful in inhibiting the SARS-CoV-2 virus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".